Adaptive filtering is widely applied in fields such as system identification, echo interference cancellation, and channel equalization, and a most commonly used method is a transversal filtering method adjusted by LMS (least mean square algorithm). Specifically, an adaptive algorithm is used to automatically correct a weight vector according to an estimation error signal, so that the error signal achieves a least mean square; however, convergence of the method decreases as a weight value of a filter increases.
In order to improve convergence of a transversal filtering method algorithm adjusted by LMS, an analysis filter bank is added in a path of an input signal, so that autocorrelation of the input signal is reduced, thereby improving the convergence of the algorithm. However, because the input signal passes through a group of analysis filters, a group of integrated filters are further needed to restore the signal when the group of filters operate; in this way, complexity of an adaptive filtering device structure is greatly increased, thereby increasing the amount of calculation of a whole adaptive filtering algorithm, so that it is difficult to apply the adaptive filtering algorithm to a digital system of high-speed processing.